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首页> 外文期刊>Transportation Research Part B: Methodological >Short-term travel behavior prediction with GPS, land use, and point of interest data
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Short-term travel behavior prediction with GPS, land use, and point of interest data

机译：利用GPS，土地利用和兴趣点数据进行短期旅行行为预测

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In everyday travel, U.S. commuters will each spend 38 h a year stuck in traffic and waste over $800 in fuel (TTI, 2015). Yet, despite this statistic, the regular commute of drivers is often predictable, leading many federal projects to aim at alleviating congestion through traveler information and intelligent transportation systems (e.g., INFLO, Queue WARN, CACC, EnableATIS, ATIS2.0). Short-term destination prediction is a developing field of research that can improve these approaches through real-traveler information, such as route, traffic incidence, and congestion levels. The short-term destination prediction problem consists of capturing vehicle Global Positioning System (GPS) traces and learning from historic locations and trajectories to predict a vehicle's destination. Drivers have predictable trip destinations that can be estimated through probabilistic modeling of past trips. To study these concepts, a database of GPS driving traces (260 participants for 70 days) was collected. To model the user's trip purpose in the prediction algorithm, a new data source was explored: point of interest (POI)/land use data. An open source land use/POI dataset is merged with the GPS dataset. The resulting database includes over 20,000 trips with travel characteristics and land use/POI data. From land use/POI data and travel patterns, trip purpose was calculated with machine learning methods. To take advantage of this data source, a new prediction model structure was developed that uses trip purpose when it is available and that falls back on traditional spatial temporal Markov models when it is not. For the first time, there is an understanding of "why" a trip is taken (not just "where" and "when"), allowing the use of "why" in the prediction model. This paper explores the baseline model followed by the inclusion of trip purpose. First, a baseline tiered time origin model was developed using the Markov Chain approach. This modelling structure allows for a short training period of current modeling techniques. The other major advantage to this structure is it allows for easy implementation of the trip purpose module. Then, a machine learning technique derived the trip purpose on 5-, 15-and 30-trip learning sets, followed by results organized by purpose, time, and origin. The machine learning technique does not require future land use data and is feasible for applicable use. This model is the first to use trip purpose to make a short-term destination prediction in pseudo real-time. Results show improved accuracy and speed over the current start-of-trip destination prediction models. (C) 2018 The Authors. Published by Elsevier Ltd.
机译：在日常出行中，美国通勤者每年每人将花费38小时堵车，浪费超过800美元的燃油（TTI，2015年）。然而，尽管有这种统计数据，驾驶员的经常上下班通常是可以预见的，这导致许多联邦项目旨在通过旅行者信息和智能交通系统（例如INFLO，Queue WARN，CACC，EnableATIS，ATIS2.0）减轻拥堵。短期目的地预测是一个发展中的研究领域，可以通过实际旅行者信息（例如路线，交通事故和拥堵程度）来改善这些方法。短期目的地预测问题包括捕获车辆的全球定位系统（GPS）轨迹以及从历史位置和轨迹中学习以预测车辆的目的地。驾驶员具有可预测的出行目的地，可以通过对过去出行的概率建模来估计。为了研究这些概念，收集了GPS行驶轨迹数据库（260名参与者，共70天）。为了在预测算法中为用户的出行目的建模，探索了一种新的数据源：兴趣点（POI）/土地使用数据。开源土地利用/ POI数据集与GPS数据集合并。最终的数据库包含超过20,000次旅行，其中包含旅行特征和土地使用/ POI数据。根据土地使用/ POI数据和出行方式，使用机器学习方法计算出出行目的。为了利用此数据源，开发了一种新的预测模型结构，该结构在可能的情况下使用出行目的，而在没有时使用传统的时空马尔可夫模型。第一次了解“为什么”旅行（而不仅仅是“哪里”和“何时”），从而允许在预测模型中使用“为什么”。本文探讨了基线模型，然后纳入了出行目的。首先，使用马尔可夫链方法开发了基线分层时间起源模型。这种建模结构可以缩短当前建模技术的培训时间。该结构的另一个主要优点是，它允许轻松实现跳闸目的模块。然后，机器学习技术在5、15和30行程学习集上得出行程目的，然后按目的，时间和起点组织结果。机器学习技术不需要将来的土地使用数据，并且对于适用的用途是可行的。该模型是第一个使用旅行目的以伪实时方式进行短期目的地预测的模型。结果表明，与当前的旅程开始目的地预测模型相比，准确性和速度有所提高。 （C）2018作者。由Elsevier Ltd.发布

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